Results 181 to 190 of about 319,642 (242)

Rapid Proteome‐Wide Discovery of Protein–Protein Interactions With ppIRIS

Advanced Science, EarlyView.
ppIRIS is a lightweight deep learning framework for proteome‐wide protein–protein interaction prediction directly from sequence. By fusing evolutionary and structural embeddings with a regularized Siamese architecture, ppIRIS achieves state‐of‐the‐art accuracy across species, enables minute‐scale screening, and reveals biologically validated bacterial ...
Luiz Felipe Piochi, Di Tang, Johan Malmström, Yasaman Karami, Hamed Khakzad   +4 more
wiley   +1 more source

City-level GDP estimates for China under alternative pathways from 2020 to 2100. [PDF]

Sci Data
Sun J, Wang R, Wang J, Zhang S, Lei M, Zhao M, Zhu J, Wang C, Cai W.   +8 more
europepmc   +1 more source

FASLG Derived from Fibroblasts in Hydroxyapatite‐Rich Microenvironment Induces Urothelial Anoikis to Trigger Randall's Plaque Exposure

Advanced Science, EarlyView.
Randall's plaques (RP) serve as the nidus for calcium oxalate (CaOx) kidney stones. The current study reveals that hydroxyapatite (HAP) crystals activate the THY1–GSK3α/β–β‐catenin axis in renal interstitial fibroblasts (hRIFs), inducing FASLG secretion.
Minghui Liu, Maolan Wu, Meng Gao, Yongchao Li, Miao Yuan, Zhangcheng Liao, Zhi Liu, Yizhou Wang, Yu Cui, Jinbo Chen, Cheng He, Zhiyong Chen, Feng Zeng, Hequn Chen, Zewu Zhu   +14 more
wiley   +1 more source

Quantum-enhanced hybrid deep reinforcement learning for real-time volleyball tactical decision making. [PDF]

Sci Rep
Cai N, Zhao M, Ke Y, Liu X.
europepmc   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

TC-HUR: A Tri-Phase Cauchy-Assisted Hunger Games Search and Unified Runge-Kutta Optimizer for Robust DNA Data Storage. [PDF]

Int J Mol Sci
Öztürk B, İgit A, Kaya A, Çamlıca ZT, Arıcı S, Şahin MF.   +5 more
europepmc   +1 more source

Metabolic Imbalance Triggers Adaptive Remodeling to Accelerate Diploidization in Murine Haploid Embryonic Stem Cells

Advanced Science, EarlyView.
In this article, Shuai and colleagues demonstrate that metabolic remodeling drives self‐diploidization in murine haploid ESCs (haESCs). Mitochondrial dysfunction and imbalanced pyruvate metabolism underlie this process. Genome‐wide screening using haESCs identifies key mitochondrial quality‐control related genes, enabling a metabolism‐based medium that
Yi Fu, Wenhao Zhang, Yifan Zhang, Yu He, Yi Du, Yiding Zhao, Chunmeng Yao, Shengyi Sun, Xiaoyan Sheng, Qian Gao, Chao Tong, Ling Shuai   +11 more
wiley   +1 more source

Hybrid ML and metaheuristic optimization of slag-fly ash-gypsum modified solidified sludge for construction. [PDF]

Sci Rep
Azarkhosh H, Chen Y, Elias S.
europepmc   +1 more source

Dynamic Shifts in ER–Plasma Membrane Junctions Signaling Define Pro‐Metastatic N‐Glycosylation and Predict Prostate Cancer Progression

Advanced Science, EarlyView.
Prostate cancer remains a leading cause of male cancer death, yet screening cannot reliably identify aggressive disease, underscoring the need for tissue biomarkers. It is shown that primary tumors increase ER–plasma membrane junction signaling via STIM1/ORP5, whereas metastasis features their loss, Golgi dispersal, and rapid conversion of high‐mannose
Amanda J. Macke, Tania Kamal, Taylor E. Divita, Artem N. Pachikov, Chad A. LaGrange, Rajesh Ravichandran, Martha Morton, Robert Powers, Haowen Qiu, Jean‐Jack M. Riethoven, Colm Morrissey, Melinda Wojtkiewicz, Rebekah L. Gundry, Carol A. Casey, Armen Petrosyan   +14 more
wiley   +1 more source

SAMTI: Sampling Adaptive Thermodynamic Integration for Alchemical Free Energy Calculations. [PDF]

J Phys Chem B
Lee TS, Jahanmahin O, Pal S, York DM.
europepmc   +1 more source